Transductive reliability estimation for kernel based classifiers

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4723 LNCS, Issue , 2007, Pages 37-47

  Tzikas, Dimitris ^a   Kukar, Matjaz ^b   Likas, Aristidis ^a  

^a UNIVERSITY OF IOANNINA   (Greece)

^b UNIVERSITY OF LJUBLJANA   (Slovenia)

Author keywords

[No Author keywords available]

Indexed keywords

BIOINFORMATICS; DATA STRUCTURES; DECISION TREES; DIAGNOSIS; PROBLEM SOLVING; SUPPORT VECTOR MACHINES;

RELEVANCE VECTOR MACHINE; TRANSDUCTIVE RELIABILITY;

CLASSIFICATION (OF INFORMATION);

EID: 38049001002     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-74825-0_4     Document Type: Conference Paper

References (13)

1. Hand, D., Mannila, H., Smyth, P.: Principles of Data Mining. MIT Press, Cambridge (2001)
2. Kukar, M., Kononenko, I.: Reliable classifications with Machine Learning. In: EIomaa, T., Mannila, H., Toivonen, H. (eds.) ECML 2002. LNCS (LNAI), vol. 2430, Springer, Heidelberg (2002)
3. Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: Proceedings of the 11th Annual Conference on Computational Learning Theory, pp. 92-100 (1998)
4. Li, M., Vitányi, P.: An introduction to Kolmogorov complexity and its applications, 2nd edn. Springer, New York (1997)
5. Dougherty, J., Kohavi, R., Sahami, M.: Supervised and unsupervised discretization of continuous features. In: Proc. ICML'95, pp. 194-202. Morgan Kaufmann, San Francisco (1995)
6. th Conference on Uncertainty in Artificial Intelligence, Madison, Wisconsin, pp. 148-155 (1998)
7. Saunders, C., Gammerman, A., Vovk, V.: Transduction with confidence and credibility. In: Proceedings of the International Joint Conference on Artificial Intelligence, Stockholm, Sweden (1999)
8. Proedrou, K., Nouretdinov, I., Vovk, V., Gammerman, A.: Transductive confidence machines for pattern recognition. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) ECML 2002. LNCS (LNAI), vol. 2430, pp. 381-390. Springer, Heidelberg (2002)
9. Kukar, M.: Quality assessment of individual classifications in machine learning and data mining. Knowledge and Information Systems 9(3), 364-384 (2006)
10. Cortes, C., Vapnik, V.: Support-vector networks. Machine Learning 20(3), 273-297 (1995)
11. Tipping, M.E.: Sparse Bayesian learning and the Relevance Vector Machine. Journal of Machine Learning Research 1, 211-244 (2001)
12. Tipping, M., Faul, A.: Fast marginal likelihood maximisation for sparse Bayesian models. In: Proc. of the Ninth International Workshop on Artificial Intelligence and Statistics (2003)
13. Kukar, M., Kononenko, I., selj, C.G., Kralj, K., Fettich, J.: Analysing and improving the diagnosis of ischaemic heart disease with machine learning. Artificial Intelligence in Medicine: Special Issue on Data Mining Techniques and Applications in Medicine (1999) (in press)